OpenRaiser/Intern-Atlas huggingface dataset:
Information-Weighted Neural Cache Language Models for ASR
Venue: arXiv cs.CL 2018
Year: 2018

Neural cache language models (LMs) extend the idea of regular cache language models by making the cache probability dependent on the similarity between the current context and the context of the words in the cache. We make an extensive comparison of 'regular' cache models with neural cache models, both in terms of perplexity and WER after rescoring first-pass ASR results. Furthermore, we propose two extensions to this neural cache model that make use of the content value/information weight of the word: firstly, combining the cache probability and LM probability with an information-weighted interpolation and secondly, selectively adding only content words to the cache. We obtain a 29.9%/32.1% (validation/test set) relative improvement in perplexity with respect to a baseline LSTM LM on the WikiText-2 dataset, outperforming previous work on neural cache LMs. Additionally, we observe significant WER reductions with respect to the baseline model on the WSJ ASR task.
A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings
Venue: arXiv cs.AI 2018
Year: 2018

This investigation aims to study different adaptive fuzzy inference algorithms capable of real-time sequential learning and prediction of time-series data. A brief qualitative description of these algorithms namely meta-cognitive fuzzy inference system (McFIS), sequential adaptive fuzzy inference system (SAFIS) and evolving Takagi-Sugeno (ETS) model provide a comprehensive comparison of their working principle, especially their unique characteristics are discussed. These algorithms are then simulated with dataset collected at one of the academic buildings at Nanyang Technological University, Singapore. The performance are compared by means of the root mean squared error (RMSE) and non-destructive error index (NDEI) of the predicted output. Analysis shows that McFIS shows promising results either with lower RMSE and NDEI or with lower architectural complexity over ETS and SAFIS. Statistical Analysis also reveals the significance of the outcome of these algorithms.
Taking Informed Action on Student Activity in MOOCs
Venue: arXiv cs.HC 2018
Year: 2018

This paper presents a novel approach to understand specific student behavior in MOOCs. Instructors currently perceive participants only as one homogeneous group. In order to improve learning outcomes, they encourage students to get active in the discussion forum and remind them of approaching deadlines. While these actions are most likely helpful, their actual impact is often not measured. Additionally, it is uncertain whether such generic approaches sometimes cause the opposite effect, as some participants are bothered with irrelevant information. On the basis of fine granular events emitted by our learning platform, we derive metrics and enable teachers to employ clustering, in order to divide the vast field of participants into meaningful subgroups to be addressed individually.
A Bayesian Approach to Multivariate Adaptive Localization in Ensemble-Based Data Assimilation with Time-Dependent Extensions
Venue: arXiv cs.NA 2018
Year: 2018

Ever since its inception, the Ensemble Kalman Filter has elicited many heuristic methods that sought to correct it. One such method is localization---the thought that `nearby' variables should be highly correlated with `far away' variable not. Recognizing that correlation is a time-dependent property, adaptive localization is a natural extension to these heuristics. We propose a Bayesian approach to adaptive Schur-product localization for the DEnKF, and extend it to support multiple radii of influence. We test both the empirical validity of (multivariate) adaptive localization, and of our approach. We test a simple toy problem (Lorenz'96), extending it to a multivariate model, and a more realistic geophysical problem (1.5 Layer Quasi-Geostrophic). We show that the multivariate approach has great promise on the toy problem, and that the univariate approach leads to improved filter performance for the realistic geophysical problem.
Fast Geometrically-Perturbed Adversarial Faces
Venue: arXiv cs.LG 2018
Year: 2018

The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications. However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model. In this study, we explore the extent to which face recognition systems are vulnerable to geometrically-perturbed adversarial faces. We propose a fast landmark manipulation method for generating adversarial faces, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models. To further force the generated samples to be natural, we introduce a second attack constrained on the semantic structure of the face which has the half speed of the first attack with the success rate of 99.96%. Both attacks are extremely robust against the state-of-the-art defense methods with the success rate of equal or greater than 53.59%. Code is available at https://github.com/alldbi/FLM
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware
Venue: arXiv cs.CR 2018
Year: 2018

Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.
Zoom-RNN: A Novel Method for Person Recognition Using Recurrent Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

The overwhelming popularity of social media has resulted in bulk amounts of personal photos being uploaded to the internet every day. Since these photos are taken in unconstrained settings, recognizing the identities of people among the photos remains a challenge. Studies have indicated that utilizing evidence other than face appearance improves the performance of person recognition systems. In this work, we aim to take advantage of additional cues obtained from different body regions in a zooming in fashion for person recognition. Hence, we present Zoom-RNN, a novel method based on recurrent neural networks for combining evidence extracted from the whole body, upper body, and head regions. Our model is evaluated on a challenging dataset, namely People In Photo Albums (PIPA), and we demonstrate that employing our system improves the performance of conventional fusion methods by a noticeable margin.
Early Identification of Pathogenic Social Media Accounts
Venue: arXiv cs.SI 2018
Year: 2018

Pathogenic Social Media (PSM) accounts such as terrorist supporters exploit large communities of supporters for conducting attacks on social media. Early detection of these accounts is crucial as they are high likely to be key users in making a harmful message "viral". In this paper, we make the first attempt on utilizing causal inference to identify PSMs within a short time frame around their activity. We propose a time-decay causality metric and incorporate it into a causal community detection-based algorithm. The proposed algorithm is applied to groups of accounts sharing similar causality features and is followed by a classification algorithm to classify accounts as PSM or not. Unlike existing techniques that take significant time to collect information such as network, cascade path, or content, our scheme relies solely on action log of users. Results on a real-world dataset from Twitter demonstrate effectiveness and efficiency of our approach. We achieved precision of 0.84 for detecting PSMs only based on their first 10 days of activity; the misclassified accounts were then detected 10 days later.
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
Venue: arXiv cs.LG 2018
Year: 2018

State representation learning aims at learning compact representations from raw observations in robotics and control applications. Approaches used for this objective are auto-encoders, learning forward models, inverse dynamics or learning using generic priors on the state characteristics. However, the diversity in applications and methods makes the field lack standard evaluation datasets, metrics and tasks. This paper provides a set of environments, data generators, robotic control tasks, metrics and tools to facilitate iterative state representation learning and evaluation in reinforcement learning settings.
Optimal Resonant Beam Charging for Electronic Vehicles in Internet of Intelligent Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

To enable electric vehicles (EVs) to access to the internet of intelligent vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent need. The resonant beam charging (RBC) technology can provide high-power and long-range wireless energy for EVs. However, the RBC system is unefficient. To improve the RBC power transmission efficiency, the adaptive resonant beam charging (ARBC) technology was introduced. In this paper, after analyzing the modular model of the ARBC system, we obtain the closed-form formula of the end-to-end power transmission efficiency. Then, we prove that the optimal power transmission efficiency uniquely exists. Moreover, we analyze the relationships among the optimal power transmission efficiency, the source power, the output power, and the beam transmission efficiency, which provide the guidelines for the optimal ARBC system design and implementation. Hence, perpetual energy can be supplied to EVs in IoIV virtually.
Fair Scheduling in Resonant Beam Charging for IoT Devices
Venue: arXiv cs.SY 2018
Year: 2018

Resonant Beam Charging (RBC) is the Wireless Power Transfer (WPT) technology, which can provide high-power, long-distance, mobile, and safe wireless charging for Internet of Things (IoT) devices. Supporting multiple IoT devices charging simultaneously is a significant feature of the RBC system. To optimize the multi-user charging performance, the transmitting power should be scheduled for charging all IoT devices simultaneously. In order to keep all IoT devices working as long as possible for fairness, we propose the First Access First Charge (FAFC) scheduling algorithm. Then, we formulate the scheduling parameters quantitatively for algorithm implementation. Finally, we analyze the performance of FAFC scheduling algorithm considering the impacts of the receiver number, the transmitting power and the charging time. Based on the analysis, we summarize the methods of improving the WPT performance for multiple IoT devices, which include limiting the receiver number, increasing the transmitting power, prolonging the charging time and improving the single-user's charging efficiency. The FAFC scheduling algorithm design and analysis provide a fair WPT solution for the multi-user RBC system.
Lookup Lateration: Mapping of Received Signal Strength to Position for Geo-Localization in Outdoor Urban Areas
Venue: arXiv cs.NI 2018
Year: 2018

The accurate geo-localization of mobile devices based upon received signal strength (RSS) in an urban area is hindered by obstacles in the signal propagation path. Current localization methods have their own advantages and drawbacks. Triangular lateration (TL) is fast and scalable but employs a monotone RSS-to-distance transformation that unfortunately assumes mobile devices are on the line of sight. Radio frequency fingerprinting (RFP) methods employ a reference database, which ensures accurate localization but unfortunately hinders scalability. Here, we propose a new, simple, and robust method called lookup lateration (LL), which incorporates the advantages of TL and RFP without their drawbacks. Like RFP, LL employs a dataset of reference locations but stores them in separate lookup tables with respect to RSS and antenna towers. A query observation is localized by identifying common locations in only associating lookup tables. Due to this decentralization, LL is two orders of magnitude faster than RFP, making it particularly scalable for large cities. Moreover, we show that analytically and experimentally, LL achieves higher localization accuracy than RFP as well. For instance, using grid size 20 m, LL achieves 9.11 m and 55.66 m, while RFP achieves 72.50 m and 242.19 m localization errors at 67\% and 95\%, respectively, on the Urban Hannover Scenario dataset.
RapidHARe: A computationally inexpensive method for real-time human activity recognition from wearable sensors
Venue: arXiv cs.CY 2018
Year: 2018

Recent human activity recognition (HAR) methods, based on on-body inertial sensors, have achieved increasing performance; however, this is at the expense of longer CPU calculations and greater energy consumption. Therefore, these complex models might not be suitable for real-time prediction in mobile systems, e.g., in elder-care support and long-term health-monitoring systems. Here, we present a new method called RapidHARe for real-time human activity recognition based on modeling the distribution of a raw data in a half-second context window using dynamic Bayesian networks. Our method does not employ any dynamic-programming-based algorithms, which are notoriously slow for inference, nor does it employ feature extraction or selection methods. In our comparative tests, we show that RapidHARe is an extremely fast predictor, one and a half times faster than artificial neural networks (ANNs) methods, and more than eight times faster than recurrent neural networks (RNNs) and hidden Markov models (HMMs). Moreover, in performance, RapidHare achieves an F1 score of 94.27\% and accuracy of 98.94\%, and when compared to ANN, RNN, HMM, it reduces the F1-score error rate by 45\%, 65\%, and 63\% and the accuracy error rate by 41\%, 55\%, and 62\%, respectively. Therefore, RapidHARe is suitable for real-time recognition in mobile devices.
Reflection On Reflection In Design Study
Venue: arXiv cs.HC 2018
Year: 2018

Visualization design study research methodologies emphasize the need for reflection to generate knowledge. And yet, there is very little guidance in the literature specifying what reflection in the context of design studies actually involves. We initiated a community discussion on this topic through a panel at the 2017 IEEE VIS Conference - this report documents the panel discussion. We analyze the panel content through the lense of our own reflective experiences and propose several priorities for ongoing thinking on reflection in applied visualization research.
Coded Caching with Shared Caches: Fundamental Limits with Uncoded Prefetching
Venue: arXiv cs.IT 2018
Year: 2018

The work identifies the fundamental limits of coded caching when the K receiving users share Λ$\leq$ K helper-caches, each assisting an arbitrary number of different users. The main result is the derivation of the exact optimal worst-case delivery time - under the assumption of uncoded cache placement - for any user-to-cache association profile, where each such profile describes how many users are helped by each cache. This is achieved with a new information-theoretic converse that is based on index coding and which proves that a simple XOR-shrinking-and-removal coded-caching scheme is optimal irrespective of the user-to-cache association profile. All the results also apply directly to the coded caching problem with multiple file requests.
Physical Layer Key Generation for Secure Power Line Communications
Venue: arXiv cs.CR 2018
Year: 2018

Leakage of information in power line communication networks is a threat to privacy and security both in smart grids and in-home applications. A way to enhance security is to encode the transmitted information with a secret key. Relying on the channel properties, it is possible to generate a common key at the two communication ends without transmitting it through the broadcast channel. Since the key is generated locally, it is intrinsically secure from a possible eavesdropper. Most of the existing physical layer key generation techniques have been developed for symmetric channels. However, the power line channel is in general not symmetric, but just reciprocal. Therefore, in this paper, we propose two novel methods that exploit the reciprocity of the power line channel to generate common information at the two intended users. This information is processed through different quantization techniques to generate secret keys. To assess the security of the generated keys, we analyze the spatial correlation of the power line channels and verify the low correlation of the possible eavesdropping channels. The two proposed methods are tested on a measurement dataset. The results show that the information leaked to possible eavesdroppers has very low correlation to any secret key.
SS5G: Collision Resolution Protocol for Delay and Energy Efficient LoRa Networks
Venue: arXiv cs.NI 2018
Year: 2018

Future 5G and Internet of Things (IoT) applications will heavily rely on long-range communication technologies such as low-power wireless area networks (LPWANs). In particular, LoRaWAN built on LoRa physical layer is gathering increasing interests, both from academia and industries, for enabling low-cost energy efficient IoT wireless sensor networks for, e.g., environmental monitoring over wide areas. While its communication range may go up to 20 kilometers, the achievable bit rates in LoRaWAN are limited to a few kilobits per second. In the event of collisions, the perceived rate is further reduced due to packet loss and retransmissions. Firstly, to alleviate the harmful impacts of collisions, we propose a decoding algorithm that enables to resolve several superposed LoRa signals. Our proposed method exploits the slight desynchronization of superposed signals and specific features of LoRa physical layer. Secondly, we design a full MAC protocol enabling collision resolution. The simulation results demonstrate that the proposed method outperforms conventional LoRaWAN jointly in terms of system throughput, energy efficiency as well as delay. These results show that our scheme is well suited for 5G and IoT systems, as one of their major goals is to provide the best trade-off among these performance objectives.
Low Complexity Full Duplex MIMO: Novel Analog Cancellation Architectures and Transceiver Design
Venue: arXiv cs.IT 2018
Year: 2018

Incorporating full duplex operation in Multiple Input Multiple Output (MIMO) systems provides the potential of boosting throughput performance. However, the hardware complexity of the analog self-interference canceller in emerging full duplex MIMO designs mostly scales with the number of transmit and receive antennas, thus exploiting the benefits of analog cancellation becomes impractical for full duplex MIMO transceivers, even for moderate number of antennas. In this paper, we present two novel architectures for the analog canceller comprising of reduced number of cancellation elements, compared to the state of the art, and simple multiplexers for efficient signal routing among the transmit and receive radio frequency chains. One architecture is based on analog taps (tap refers to a line of fixed delay, variable phase shifter, and attenuator) and the other on AUXiliary (AUX) Transmitters (TXs) that locally generate the cancellation signal. In contrast to the available analog cancellation architectures, the values for each tap or each AUX TX and the configuration of the multiplexers are jointly designed with the digital transmit and receive beamforming filters according to certain performance objectives. Focusing on a narrowband flat fading channel model as an example, we present a general optimization framework for the joint design of analog self-interference cancellation and digital beamforming. We also detail the sum rate optimization objective together with its derived solution for the latter architectural components. Representative computer simulation results demonstrate the superiority both in terms of hardware complexity and achievable performance of the proposed low complexity full duplex MIMO schemes over the lately available ones.
Enhanced Session Initiation Protocols for Emergency Healthcare Applications
Venue: arXiv cs.CR 2018
Year: 2018

In medical emergencies, an instant and secure messaging is an important service to provide quality healthcare services. A session initiation protocol (SIP) is an IP-based multimedia and telephony communication protocol used to provide instant messaging services. Thus, design of secure and efficient SIP for quality medical services is an emerging problem. In this paper, we first explore the security limitations of the existing SIPs proposed by Sureshkumar et al. and Zhang et al. in the literature. Our analysis shows that most of the existing schemes fail to protect the user credentials when unexpectedly the session-specific ephemeral secrets revealed to an adversary by the session exposure attacks. We then present a possible improvement over Sureshkumar et al.'s scheme without increasing the computational cost. We compare the proposed improvement for computational overheads and security features with the various related existing schemes in the literature.
BlockPKI: An Automated, Resilient, and Transparent Public-Key Infrastructure
Venue: arXiv cs.CR 2018
Year: 2018

This paper describes BlockPKI, a blockchain-based public-key infrastructure that enables an automated, resilient, and transparent issuance of digital certificates. Our goal is to address several shortcomings of the current TLS infrastructure and its proposed extensions. In particular, we aim at reducing the power of individual certification authorities and make their actions publicly visible and accountable, without introducing yet another trusted third party. To demonstrate the benefits and practicality of our system, we present evaluation results and describe our prototype implementation.